Methods of oil and gas exploration using digital imaging

ABSTRACT

Methods of oil and gas exploration that may include: obtaining wavefield data representing recordings from a propagating wavefield through a geophysical volume; obtaining at least one reference digital image of a portion or all of the geophysical volume generated from the recorded wavefield data, wherein the reference image may have a reference sampling ratio and a reference image quality value; selecting a holographic computational method of imaging the wavefield data; selecting a data subset from the wavefield data based on one or more parameters selected from the group consisting of field sampling, imaging sampling, and image quality; decimating the data subset, wherein the decimated data subset may represent a sampling ratio less than the reference sampling ratio; and generating a new digital image based on the selected holographic computational method of imaging, the data subset, and parameters corresponding to the data sub set.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Nonprovisional applicationSer. No. 16/905,073, filed on Jun. 18, 2020, which claims benefit toU.S. Provisional Application 62/863,123, filed on Jun. 18, 2019; andthis application hereby incorporates herein U.S. Nonprovisionalapplication Ser. No. 16/905,073 and U.S. Provisional Application62/863,123 as if set forth herein in their entireties.

BACKGROUND 1. Field of Inventions

The field of this application and any resulting patent is oil and gasexploration, preferably using digital imaging.

2. Description of Related Art

Various methods of providing digital images have been proposed andutilized, including the methods and systems disclosed in the referencesappearing on the face of this patent. However, these methods and systemslack all the steps or features of the methods and devices covered by thepatent claims below. Furthermore, the methods and systems covered by atleast some of the claims of this issued patent may solve many of theproblems that prior art methods and systems have failed to solve. Also,the methods and systems covered by at least some of the claims of thispatent may have benefits that would be surprising and unexpected to aperson of ordinary skill in the art based on the prior art existing atthe time of the inventions set forth in one or more of the claimsherein.

SUMMARY

Disclosed herein are methods of oil and gas exploration that mayinclude: 1) obtaining wavefield data representing recordings of apropagating wavefield through a geophysical volume; 2) obtaining areference digital image of a portion or all of the geophysical volumegenerated from the wavefield data, wherein the reference image may havea reference sampling ratio and a reference image quality value; 3)selecting a holographic computational method of imaging the wavefielddata from a group consisting of the Kirchhoff diffraction stackingmethod, the Kirchhoff wave front “smear” method, wavefield synthesis,and wave equation-based methods; 4) selecting a data subset from thewavefield data based on one or more parameters selected from the groupconsisting of field sampling, imaging sampling, and image quality; 5)calculating a sampling ratio by dividing a number of data samples in thedata subset by a number of image samples in the data subset; 6)decimating the data subset, wherein the decimated data subset mayrepresent a sampling ratio less than the reference sampling ratio; 7)generating a new digital image based on the selected holographiccomputational method of imaging, the decimated data subset, andparameters corresponding to the data subset selected from the groupconsisting of environmental parameters, legal parameters, operationalparameters, financial parameters, and safety parameters, wherein the newdigital image may have a new image quality value greater than thereference image quality value; and 8) determining a quantitativedifference measure between the reference digital image and the newdigital image based on changing one or more parameters selected from thegroup consisting of field sampling, imaging sampling, and image quality.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an imaging system.

FIG. 2 illustrates general steps of a process for providing a digitalimage.

FIG. 3 illustrates an exemplary placement configuration of sources andreceivers on a propagation volume for wavefield acquisition.

FIG. 4A illustrates voxel contributions for a source-receiver pair.

FIG. 4B illustrates voxel contributions for another source-receiverpair.

FIG. 4C illustrates a voxel in an image volume.

FIG. 5A illustrates a voxel in a propagation medium illuminated by awavefield having a source directly above it.

FIG. 5B illustrates the voxel in a propagation medium illuminated by awavefield having a source directly above it showing the reflectedwavefield from it.

FIG. 5C illustrates a hyperboloid of revolution surface representing acommon source-receiver separation and three exemplary intersectingrecorded traces, M1, M2, and M3.

FIG. 6 illustrates a half ellipsoid of revolution surface formed in animage volume.

FIG. 7A illustrates steps for selecting data samples.

FIG. 7B illustrates technical parameters used to select and process datasamples to generate a digital image.

FIG. 8A illustrates steps for processing data samples into a digitalimage.

FIG. 8B illustrates business environment parameters by category used toprocess data samples to generate a digital image.

FIG. 9 illustrates images of a reflection wavefield from the transitionzone subsurface survey undertaken along the Louisiana coastal transitionzone.

FIG. 10 illustrates images of a propagating wavefield sampled atdifferent spatial and time intervals.

FIG. 11 illustrates images of a seismic survey of a portion of the TexasGlenrose Reef using inversion information and display formats includingan Extended Visual Dynamic Range color display format.

FIG. 12 illustrates images of a seismic survey of a portion of theAustin Chalk using inversion information and display formats includingan Extended Visual Dynamic Range color display format.

FIG. 13 illustrates images of a seismic survey of a portion of the EagleFord formation using inversion information and display formats includingan Extended Visual Dynamic Range color display format.

DETAILED DESCRIPTION 1. Introduction

A detailed description will now be provided. The purpose of thisdetailed description, which includes the drawings, is to satisfy thestatutory requirements of 35 U.S.C. § 112. For example, the detaileddescription includes a description of the inventions disclosed hereinand sufficient information that would enable a person having ordinaryskill in the art to make and use the inventions. In the figures, likeelements are generally indicated by like reference numerals regardlessof the view or figure in which the elements appear. The figures areintended to assist the description and to provide a visualrepresentation of certain aspects of the subject matter describedherein. The figures are not all necessarily drawn to scale, nor do theyshow all the structural details of the systems, nor do they limit thescope of the disclosure herein.

2. Selected Definitions

Certain terms as used herein are expressly defined below.

It will be apparent that the various terms identified and/or definedbelow may be embodied in methods, systems, and/or non-transient,computer-readable media (CRM), e.g., as part of a set ofmachine-readable instructions (object code) residing in some type ofcomputer hardware, e.g., processors or memory. The various ways to usethe items represented by the terms discussed below, and to implement theequations, calculations, and algorithms described herein, will be knownor can otherwise be determined by persons skilled in the art of computerprogramming based on this patent application disclosure, particularlythose who are familiar with writing computer programs, includingcomputer programs relating to optimizing digital imaging. Also, theitems discussed below, may be implemented in a variety of differenttypes of computer programs using any one of a number of differentprogramming languages; and the methods, systems, and CRM are not limitedto any particular computer program or programming language.

The term “attribute” as used herein is defined as a quality, property,or characteristic of something.

The term “acquisition” as used herein is defined as an act of.

The term “data” as used herein is defined as information, e.g.,information associated with one or more nouns, e.g., an entity. A firstportion of data may have a relationship with a second portion of data.

The term “database” as used herein is defined as a structured set ofdata, e.g., information stored on a digital storage medium or a computerconfigured for rapid storage, grouping, query, manipulation, and/orpresentation of the data. A database may have one or more structures,e.g., tables, views, or synonyms. A database may have one or morestructures configured for grouping and/or presentation of data. Adatabase may have instructions, e.g., stored procedures, configured torapidly read, fetch, insert, update, and/or delete data in a table.

The term “digital” as used herein is defined as a series of numbers,e.g., 0's and 1's.

The term “digital image” as used herein is defined as a numericrepresentation of one or more properties of a two-dimensional area orthree-dimensional volume.

The term “exemplary” is used exclusively herein to mean “serving as anexample, instance, or illustration.” Anything, including any embodiment,structure, element, or step, described herein as exemplary, is not to beconstrued as preferred or advantageous over other embodiments,structures, elements, steps, etc.

The term “field sampling” as used herein is defined as data representingan identifier and/or position of a detector used to detect a propagatingwavefield.

The terms “he,” “she,” “they,” and any other personal pronouns as usedherein refer to any gender interchangeably. For example, all uses of“he” encompasses “she” as well.

The term “holographic computational method” as used herein is defined asa method of processing encoded data representing wavefields propagatingwithin a propagation volume. The encoded data may include holographicencoded data. Holographic computational methods may include, e.g., theKirchhoff diffraction stacking method, the Kirchhoff wavefront smearmethod, wavefield synthesis, and wave equation-based methods.

The term “identifier” as used herein is defined as one or more datavalues that uniquely identify and/or distinguish a record from any otherrecord stored in a table. An identifier may be referred to as a primarykey.

The term “image sampling” as used herein is defined as a number ofpixels or voxels defining a digital image or volume.

The term “optimize” as used herein means to improve. For example,optimizing production of a digital image may include one or more stepsto rearrange, rewrite, and/or decimate data and/or operations, toimprove efficiency of the production. Optimize may include a step tocalculate a value of a distance measured in a multivariate criteriaspace. A multivariate criteria space may include any one or more of anumber of components of technical parameters and business environmentparameters. Technical parameters may include, e.g., computationalmethods, field samplings, image samplings, and image quality. Businessenvironment parameters may environmental parameters, legal parameters,operational parameters, financial parameters, and safety parameters.

The term “parameter” as used herein is defined as a variable orargument, e.g., used in a function or subroutine. Types of parametersmay include, e.g., technical parameters and business environmentparameter. Technical parameters may include, e.g., computational methodsparameters, field sampling parameters, image sampling parameters, andimage quality parameters. Business environment parameter may include,e.g., legal parameters, operational parameters, financial parameters,and safety parameters. A parameter may have a value, e.g., number.

The term “quantitative difference distance measure” as used herein isdefined as a multivariate value, e.g., quantity. A set of quantitativedifference distance measure may be ranked.

The term “sampling ratio” as used herein is defined as a ratio of asample size or a population size. For example, sampling ratio ofwavefield recordings may be equal to a number of data samples in a datasubset divided by a number of image samples in the data subset.

The term “propagation volume” as used herein is defined as a portion ofa body, structure, or space capable of receiving energy waves, e.g.,sound, vibration, or light. A propagation volume may receive apropagating wavefield. A propagation volume may be animate or inanimate.For example, an animate propagation volume may be a portion of a humanbody, e.g., head, torso, or limbs, or some other living tissue, orformerly living tissue. Thus, the methods disclosed herein have medicalapplicability in which a propagation volume is some portion of the humanbody such as the brain, a breast, or a lung. An inanimate propagationvolume may be a geophysical volume of the earth, e.g., underground oilfield, ocean bed, or bedrock. Accordingly, oil and gas explorationmethods are disclosed that include the providing of digital imaging inwhich the propagation volume is a geophysical volume. Also disclosedherein are methods for identifying subterranean tunnels, includingcaverns, mineshafts, or buried conduits including the providing ofdigital images. Additionally, an inanimate propagation volume may be aportion of a celestial body, e.g., asteroid, comet, moon, or planet.Accordingly, methods for identifying flying objects are disclosed wherethe propagation volume is some non-solid space above the earth'ssurface, which may be any part of the atmosphere, such that the flyingobjects may be aircraft such as drones or airplanes, but the propagationvolume may also be above the earth's atmosphere, including outer space,wherein electromagnetic signals can be used to form images of flyingobjects such as manned bodies or projectiles. Types of propagationvolume may include, e.g., a geophysical volume and a Cartesian volume. A“geophysical volume” may be a portion of the earth having a length,width, and depth. A “Cartesian volume” may have positions within it thatcan described by an orthogonal coordinate system, which may be denotedas X, Y, and Z, for example. A propagation volume may be representeddigitally, e.g., as an image volume and/or data volume. An image volumemay incorporate all or some representation of a propagation volume inwhich some property related to the propagation volume is representedvisually as an image. Also, a data volume or data capture may be anorganized representation of digitally recorded wavefield data forcomputations. A data volume may be coincident in whole or part with thepropagation volume and/or the image volume. Entries within a data volumemay be of matrix or vector nature, and multivalued as well in dependingon source and receiver locations.

The term “receiver” as used herein is defined as a device for receivingand/or recording propagating wavefields.

The term “source” as used herein is defined as a device for dischargingenergy into a propagation volume.

The term “table” as used herein is defined as a logical structure, e.g.,grouping, of data in a database configured for rapid grouping, query,and/or manipulation of the data. A table may represent a real-worldgroup of things, e.g., signal source, signal receiver, voxel, question,and/or answer. Each thing may have attributes or characteristics, e.g.,dimension or direction. Each thing may be represented by a record in atable. Each attribute of each thing may be represented by a field in thetable. Each record on each data table may or, in some cases, may nothave data that must conform to the rules of a table definition. A fieldfor each attribute must conform to certain data type constraints, e.g.,always be a numeral, a string of letters and/or characters, or a binaryvalue. Additionally, a field for each attribute must conform to certainvalue or format constraints, e.g., be non-repetitive, be uniquely named,and/or always have some value or a default value. Each record may bereferred to as a row. Each field may be referred to as a column. A firstrecord in a first table may be associated with, e.g., related or linkedto, a second record in a second table via a foreign key, e.g., one ormore common fields.

The term “value” as used herein is defined as one or more symbolsassigned to and identifying a quality, characteristic, and/or quantityof a thing, e.g., entity. A symbol may be, e.g., a number, a letter, apicture, a character, or anything perceptible. A value may be stored ina field in a table of a database. The term “value” covers all of thosedifferent types of values, and others as well. In the context of certainmethods, systems, and CRM described herein, any “value” that is used,whether provided or determined, e.g., calculated, may take the form of avalue that is part of the instructions of a computer program, and alsocan be an electronic value stored in computer memory or on some CRM.

The term “voxel” as used herein is defined as an array of elements ofvolume that constitute a notional three-dimensional volume. A voxel mayrepresent a portion of a propagation volume. A voxel may be cubic innature. A voxel may include attributes, e.g., size, dimensions,location, to constitute a component of an image volume. One or moreinteractions between a voxel and a digitized wavefield may be simulated.A voxel may be used to approximate one or more properties of athree-dimensional volume. A voxel may have a unique identifier having asource identifier, a receiver identifier, and/or time.

The term “wavefield” as used herein is defined as a disturbance, e.g.,propagating energy, within a propagation volume. A wavefield may begenerated by a wavefield source. A wavefield may be recorded by areceiver. The term “propagating wavefield” as used herein is defined asa disturbance propagating in at least one spatial coordinate of apropagation volume.

3. Certain Specific Embodiments

Disclosed herein are methods of oil and gas exploration that mayinclude: 1) obtaining wavefield data representing recordings of apropagating wavefield through a geophysical volume; 2) obtaining areference digital image of a portion or all of the geophysical volumegenerated from the wavefield data, wherein the reference image may havea reference sampling ratio and a reference image quality value; 3)selecting a holographic computational method of imaging the wavefielddata from a group consisting of the Kirchhoff diffraction stackingmethod, the Kirchhoff wave front “smear” method, wavefield synthesis,and wave equation-based methods; 4) selecting a data subset from thewavefield data based on one or more parameters selected from the groupconsisting of field sampling, imaging sampling, and image quality; 5)calculating a sampling ratio by dividing a number of data samples in thedata subset by a number of image samples in the data subset; 6)decimating the data subset, wherein the decimated data subset mayrepresent a sampling ratio less than the reference sampling ratio; 7)generating a new digital image based on the selected holographiccomputational method of imaging, the decimated data subset, andparameters corresponding to the data subset selected from the groupconsisting of environmental parameters, legal parameters, operationalparameters, financial parameters, and safety parameters, wherein the newdigital image may have a new image quality value greater than thereference image quality value; and 8) determining a quantitativedifference measure between the reference digital image and the newdigital image based on changing one or more parameters selected from thegroup consisting of field sampling, imaging sampling, and image quality.

In addition, disclosed herein are methods of providing a digital imageof a geophysical volume that may include: 1) obtaining wavefield datarepresenting recordings from a propagating wavefield through thegeophysical volume; 2) obtaining at least one reference digital image ofa portion or all of the geophysical volume generated from the wavefielddata, wherein the reference image has a reference sampling ratio and areference image quality value; 3) selecting a holographic computationalmethod of imaging the wavefield data; 4) selecting a data subset fromthe wavefield data based on one or more parameters selected from thegroup consisting of field sampling, imaging sampling, and image quality;5) decimating the data subset, wherein the decimated data subsetrepresents a sampling ratio less than the reference sampling ratio; and6) generating a subsequent digital image based on the selectedholographic computational method of imaging, the data subset, andparameters corresponding to the data subset selected from the groupconsisting of environmental parameters, legal parameters, operationalparameters, financial parameters, and safety parameters, wherein thesubsequent digital image has a new image quality value greater than thereference image quality value.

Also, disclosed herein are methods for identifying a flying object usingdigital imaging that may include: 1) obtaining wavefield datarepresenting recordings of a propagating wavefield through a propagatingvolume that may include a volume above the earth's surface; 2) obtaininga reference digital image of a portion or all of the propagating volumegenerated from the wavefield data, wherein the reference image may havea reference sampling ratio and a reference image quality value; 3)selecting a holographic computational method of imaging the wavefielddata from a group consisting of the Kirchhoff diffraction stackingmethod, the Kirchhoff wave front “smear” method, wavefield synthesis,and wave equation-based methods; 4) selecting a data subset from thewavefield data based on one or more parameters selected from the groupconsisting of field sampling, imaging sampling, and image quality; 5)calculating a sampling ratio by dividing a number of data samples in thedata subset by a number of image samples in the data subset; 6)decimating the data subset, wherein the decimated data subset mayrepresent a sampling ratio less than the reference sampling ratio; 7)generating a new digital image that may include a flying object passingthrough at least a portion of the propagating volume based on theselected holographic computational method of imaging the decimated datasubset, and parameters corresponding to the data subset selected fromthe group consisting of environmental parameters, legal parameters,operational parameters, financial parameters, and safety parameters,wherein the new digital image has a new image quality value greater thanthe reference image quality value; and 8) determining a quantitativedifference measure between the reference digital image and the newdigital image based on the changing of one or more parameters selectedfrom the group consisting of field sampling, imaging sampling, and imagequality.

Additionally, disclosed herein are methods for identifying asubterranean tunnel using digital imaging that may include: 1) obtainingwavefield data representing recordings of a propagating wavefieldthrough a propagating volume that may include a portion of the earth'ssubsurface; 2) obtaining a reference digital image of a portion or allof the propagating volume generated from the wavefield data, wherein thereference image may have a reference sampling ratio and a referenceimage quality value; 3) selecting a holographic computational method ofimaging the wavefield data from a group consisting of the Kirchhoffdiffraction stacking method, the Kirchhoff wave front “smear” method,wavefield synthesis, and wave equation-based methods; 4) selecting adata subset from the wavefield data based on one or more parametersselected from the group consisting of field sampling, imaging sampling,and image quality; 5) calculating a sampling ratio by dividing a numberof data samples in the data subset by a number of image samples in thedata subset; 6) decimating the data subset, wherein the decimated datasubset may represent a sampling ratio less than the reference samplingratio; 7) generating a new digital image that may include a subterraneantunnel passing through at least a portion of the propagating volumebased on the selected holographic computational method of imaging thedecimated data subset, and parameters corresponding to the data subsetselected from the group consisting of environmental parameters, legalparameters, operational parameters, financial parameters, and safetyparameters, wherein the new digital image has a new image quality valuegreater than the reference image quality value; 8) determining aquantitative difference measure between the reference digital image andthe new digital image based on the changing of one or more parametersselected from the group consisting of field sampling, imaging sampling,and image quality; and 9) identifying the subterranean tunnel.

Further, disclosed herein are methods of digital imaging of livingtissue that may include: 1) obtaining wavefield data representingrecordings of a propagating wavefield through living tissue; 2)obtaining a reference digital image of a portion or all of the livingtissue generated from the wavefield data, wherein the reference imagemay have a reference sampling ratio and a reference image quality value;3) selecting a holographic computational method of imaging the wavefielddata from a group consisting of the Kirchhoff diffraction stackingmethod, the Kirchhoff wave front “smear” method, wavefield synthesis,and wave equation-based methods; 4) selecting a data subset from thewavefield data based on one or more parameters selected from the groupconsisting of field sampling, imaging sampling, and image quality; 5)calculating a sampling ratio by dividing a number of data samples in thedata subset by a number of image samples in the data subset; 6)decimating the data subset, wherein the decimated data subset mayrepresent a sampling ratio less than the reference sampling ratio; 7)generating a new digital image based on the selected holographiccomputational method of imaging, the decimated data subset, andparameters corresponding to the data subset selected from the groupconsisting of legal parameters, operational parameters, financialparameters, and safety parameters, wherein the new digital image mayhave a new image quality value greater than the reference image qualityvalue; and 8) determining a quantitative difference measure between thereference digital image and the new digital image based on the changingof one or more parameters selected from the group consisting of fieldsampling, imaging sampling, and image quality.

Disclosed herein are methods of providing a digital image of apropagation volume, which method may include: 1) obtaining wavefielddata representing recordings of a propagating wavefield through thepropagation volume; 2) obtaining at least one reference digital image ofa portion or all of the propagation volume generated from the wavefielddata, wherein the reference image has a reference sampling ratio and areference image quality value; 3) selecting a holographic computationalmethod of imaging the wavefield data; 4) selecting a data subset fromthe wavefield data based on one or more parameters selected from thegroup consisting of field sampling, imaging sampling, and image quality;5) decimating the data subset, wherein the decimated data subsetrepresents a sampling ratio less than the reference sampling ratio; and6) generating an new digital image based on the selected holographiccomputational method of imaging, the data subset, and parameterscorresponding to the data subset selected from the group consisting ofenvironmental parameters, legal parameters, operational parameters,financial parameters, and safety parameters, wherein the new digitalimage has a new image quality value greater than the reference imagequality value.

In any one of the methods disclosed herein, the computational method ofimaging the wavefield data is selected from a group consisting of theKirchhoff diffraction stacking method, the Kirchhoff wave front “smear”method, wavefield synthesis, and wave equation-based methods.

In any one of the methods disclosed herein, the sampling ratio is equalto a number of data samples in the data subset divided by a number ofimage samples in the data subset.

Any one of the methods disclosed herein may further include determininga difference between the reference digital image and the subsequentdigital image based on one or more parameters selected from the groupconsisting of field sampling, imaging sampling, and image quality.

Any one of the methods disclosed herein may further include determininga quantitative difference measure between the reference digital imageand the subsequent digital image based on one or more parametersselected from the group consisting of field sampling, imaging sampling,and image quality.

Any one of the methods disclosed herein may further include implementingan imaging survey on another propagation volume based on a configurationof source array and receiver array corresponding to the data subset.

Any one of the methods disclosed herein may further include positioningon another geophysical volume an array of sources and an array ofreceivers based on the data subset.

Any one of the methods disclosed herein may further include positioningin another geophysical volume an array of sources and an array ofreceivers based on the data subset.

Any one of the methods disclosed herein may further include propagatinga wavefield through another geophysical volume with an array of sourceshaving a configuration based on the data subset.

Any one of the methods disclosed herein may further include receiving apropagating wavefield from another geophysical volume with an array ofreceivers having a configuration based on the data subset.

Any one of the methods disclosed herein may further include recording apropagating wavefield from another geophysical volume with an array ofreceivers having a configuration based on the data subset.

Any one of the methods disclosed herein may further include positioningon another propagation volume an array of sources and an array ofreceivers based on the data subset.

Any one of the methods disclosed herein may further include positioningin another propagation volume an array of sources and an array ofreceivers based on the data subset.

Any one of the methods disclosed herein may further include propagatinga wavefield through another propagation volume with an array of sourceshaving a configuration based on the data subset.

Any one of the methods disclosed herein may further include receiving apropagating wavefield from another propagation volume with an array ofreceivers having a configuration based on the data subset.

Any one of the methods disclosed herein may further include recording apropagating wavefield from another propagation volume with an array ofreceivers having a configuration based on the data subset.

4. Specific Embodiments in the Drawings

The drawings presented herein are for illustrative purposes only and donot limit the scope of the claims. Rather, the drawings are intended tohelp enable one having ordinary skill in the art to make and use theclaimed inventions.

This section addresses specific embodiments of the inventions shown inthe drawings, which relate to methods, systems, and computer-readablemedia (CRM) for optimizing digital imaging. Although this sectionfocuses on the drawings herein, and the specific embodiments found inthose drawings, parts of this section may also have applicability toother embodiments not shown in the drawings. The limitations referencedin this section should not be used to limit the scope of the claimsthemselves.

The processing of data for the purposes optimizing digital images ofpropagation volumes described below may be performed using one or moreprocessors distributed over various devices of an imaging system 100,i.e., a type of hardware and software infrastructure. Additionally, anyprocessing and functionality of or relating to any aspects of any of themethods, systems or CRM herein can be implemented using conventional orspecial-purpose hardware, software, and/or firmware. Data handled viasuch processing or created resulting from such processing can be storedin any type of memory and/or readable media as is conventional in theart. By way of example, such data may be stored in a temporary memory,such as in the RAM of a given computer system or subsystem. In addition,or in the alternative, such data may be stored in longer term storagedevices, such as magnetic disks, rewritable optical disks, and so on.For purposes of the disclosure herein, a computer-readable medium (CRM)may comprise any form of data storage mechanism, including existingmemory technologies as well as hardware or circuit representations ofsuch structures and of such data. Client devices, server devices, andnetwork devices may include processors, memory, and storage devices,among other components.

The techniques used with the exemplary systems and methods may beimplemented using a variety of technologies. For example, the systemsand methods described herein may be implemented in software running on aprogrammable processor or may be implemented in hardware utilizing acombination of microprocessors and other specially designedapplications, specific integrated circuits, programmable logic devices,or various combinations thereof. Also, the methods described herein maybe implemented by a series of computer-executable instructions residingon a storage medium such as a carrier wave, disk drive, or othercomputer-readable media.

FIG. 1 is a schematic diagram of an imaging system 100 implemented on anetwork of client devices and server devices. The network of clientdevices and server devices may be interconnected. The computer hardwareand system connectivity of FIG. 1 are illustrative and various otherconfigurations of devices and components may be used to carry out someor all the methods disclosed herein for acquiring data relating toseismic surveys and generating optimized digital images based on theacquired data, e.g., as described in certain embodiments herein. Theimaging system 100 may include any one or more of several clientdevices, server devices, and/or network devices. Client devices mayinclude any device, e.g., personal computer, laptop, smart phone,tablet, thin-clients, or web-based appliance, capable of operating a webbrowser, displaying interfaces, and/or executing software instructions.Server devices may include any device, e.g., web server, applicationserver, database server, or authentication server, capable of receivingand responding to requests made by client devices or other serverdevices. Network devices may include any device, e.g., firewall, hub,bridge, repeater, switch, or router, capable of transmitting requestsand responses between devices on the imaging system 100. The networkdevices may be connected to various other networks, e.g., internet,intranet, or virtual private network.

The process of data collection, handling, and processing by the system100 may be distributed among various devices that may be part of theimaging system 100. Executable instructions may be installed on each ofthe devices to receive, transfer, store, and/or process the data. Eachdevice or set of devices may represent a logical layer through whichdata may flow and be processed.

FIG. 1 illustrates an exemplary set of logical layers 102-112 of theimaging system 100. In the client layer 102, client devices may displaygraphical user interfaces via web browsers or desktop applications,through which a user may input data, review data, and view the resultsof processed data. Web servers clustered in a web farm in thepresentation layer 104 may receive and respond to requests originatingfrom client devices running in the client layer 102. Also, thepresentation layer 104 may be a first of various layers used toauthenticate and validate user authority to access the imaging system100. Application servers of the application layer 106 may receive dataand requests originating from the presentation layer 104 and beinstructed to process data, which may be returned to the presentationlayer 104. Alternatively, the application layer 106 may receive data andrequests originating directly from the client layer 104, i.e., fromsmart phones and devices running desktop applications. Additionally, theserver devices of the application layer 106 may be instructed to sendinstructions to servers of the data storage layer 108 to store data,e.g., raw data and/or processed data, for later retrieval. Furthermore,server devices of the application layer 106 may be instructed to requestdata from other application layers 110 and 112, e.g., third-partyplatforms, intranet systems, and/or extra-network systems. Arrows inFIG. 1 depict paths of data flow between different layers of the imagingsystem 100. The arrows represent one or more network connections, e.g.,as part of an internet, intranet, extranet, and/or virtual privatenetwork.

Although the imaging system 100 may be described as executing orperforming steps or functions, those skilled in the art shouldunderstand that an individual or person is responsible for the action ofthe imaging system 100. For instance, a programmer may implementsoftware which may instruct the imaging system 100 to automaticallyperform one or more steps or functions based on some input, e.g., from aperson, another system, or clock.

FIG. 2 illustrates a flowchart diagram of steps to optimizing digitalimaging of propagation volumes, e.g., geophysical volumes. The steps maybe grouped as: 1) capturing wavefields (exemplary steps 202-210), 2)generating and selecting a baseline digital image (exemplary steps212-216), and 3) generating an optimized digital image (exemplary steps218-228).

The group of steps for capturing wavefields may include: (a) step 202for arranging an array of sources and an array of receivers in a firstsurvey field (“Field1”), e.g., geophysical field; (b) step 204 fordischarging, one or more times, some or all of the sources of the arrayof sources; (c) step 206 for capturing wavefields with the receivers ofthe array of receivers; (d) step 208 for digitizing captured wavefieldas data samples; and (e) step 210 for storing the data samples.

Steps 212-216 for generating and selecting a baseline digital image maybe performed by the imaging system 100. However, in some cases, steps212-216 may be performed by one or more systems unrelated to and/ordisconnected from the imaging system 100. In those cases, data samplesof wavefield captures and baseline images resulting from those datasamples may be acquired and copied to one or more databases of thesystem 100.

The group of steps for generating a baseline digital image include: (a)step 212 for processing data representing a first subset of samples(“Samples₁”) from the data samples of step 208; (b) step 214 forgenerating a first set of digital images (“Images₁”) based on Samples1;and (c) step 216 for selecting a baseline digital image (“Image₁”) fromthe first set of digital images, Images₁.

The group of steps 218-228 for generating an optimized digital image mayinclude: (a) step 218 for selecting a holographic computational method;(b) step 220 for selecting, from the data samples of step 208, datarepresenting a subsequent subset of samples (“Samples_(i)”) based ontechnical parameters (see FIG. 7B); (c) step 222 for processing theselected data dependent on a set of business environment parameters (seeFIG. 8B); (d) step 224 for generating a subsequent set of digital images(“Images_(i)”); (e) step 226 for determining whether any image ofImages_(i) is more optimized than Image₁; and (f) step 228 for repeatingsteps 218-226 until an image of Images_(i) has a value indicatinggreater optimization than Image1 or all holographic computationalmethods have been selected and executed.

If a new image of Images_(i) is determined to be more optimized thanImage₁, later seismic surveys at a second survey field (“Field₂”) may beconducted in step 230 implementing an array of sources and an array ofreceivers in the second survey field based on one or more configurationsassociated with Samples_(i) for subsequent digital images generated.Additionally, the later seismic surveys may also be conducted based onbusiness environment parameters associated with Samples_(i).

Capturing Wavefield Data

Referring to the group of steps 202-210 for capturing wavefields in apropagation volume in the flowchart of FIG. 2 , field engineers mayfirst arrange an array of sources and an array of receivers in a firstsurvey field (Field₁) in step 202. An exemplary arrangement on thesurface of a propagation volume, e.g., oil and gas field, described byCartesian coordinates is shown in FIG. 3 . A source is shown as atriangle. A receiver is shown as a circle.

In step 204, the field engineers may discharge, one or more times, someor all of the sources of the array of sources. Each discharging sourcemay discharge energy, e.g., sound and/or light, into the propagationvolume, away from source (see FIG. 5A). The discharged energy maypropagate through the propagation volume. Various bodies, e.g., rocks,water, and hydrocarbon, may receive the energy. Each body may cause thepropagating energy to be reflected and/or diffracted from the body. Thereflected and/or diffracted energy may be propagated towards a receiver(see FIG. 5B). The discharged energy reflected and/or diffracted withinthe propagation volume, e.g., towards a receiver, may be referred to asa propagating wavefield.

In step 206, receivers in the array of receivers on the surface of thepropagation volume may receive the propagating wavefield. Additionally,each receiver may record one or more characteristics of the propagatingwavefields, e.g., acceleration, velocity, and/or direction, from eachsource that discharged.

In step 208, each receiver of the array of receivers may record one ormore characteristics of the propagating wavefields, e.g., acceleration,velocity, and/or direction, that it receives by digitizing, e.g.,converting energy signals from propagating wavefields, into digitaldata. The digital data may include information representing anidentifier for the receiver, an identifier for the source, frequency ofthe energy received, and amplitude of the energy received.

In step 210, each receiver of the array of receivers may send thedigitized data to a database server. The database server may assign aunique identifier representing the receiver, a source, the receiver, andone or more portions of the propagating wavefield received by thereceiver.

The speed of travel for electromagnetic signals (like light or radar) inpropagating wavefields requires sampling in nanoseconds or smaller timescales, which may generate large amounts of data in the magnitude ofterabytes or larger. Because of computer technology limitations,previous imaging systems could only be implemented by analog methods,using coherent wavefield sources like lasers, and photographicrecording. Hence, there may still some prevailing association of theterm holography with optical methods. However, with the current digitalimaging system 100 as illustrated in FIG. 1 , digital methods may now beapplied. Moreover, modern digital ground penetrating radar systems are agood example of such technology using electromagnetic wavefields.Current hardware and technology such as those implemented in the imagingsystem 100 may have the computational and storage capacity to processterabytes or more of data. Thus, in step 210 of FIG. 2 , the digitizeddata may be stored on a database. The database may be part of theimaging system 100 or a on separate system accessible by the imagingsystem 100.

Wavefields of various types may be used for a variety of importantapplications. A wavefield may propagate in time within a Cartesianvolume. Using digitized recordings in space and time of wavefields,digital images may be constructed based on data representing propagationvolumes. An image volume then coincides with the propagation volume inwhole or part. The propagating wavefield as recorded, may represent a“wavefield capture” or a “data capture,” which may be discretely sampledin all dimensional variables (X, Y, Z and T). These recordingscollectively may constitute yet a third volume, e.g., a data volume.

Generating or Acquiring a Baseline Image

The imaging system 100 may be capable of performing steps 212-216 forgenerating a baseline image. However, it should be understood that oneor more separate systems, independent of the imaging system 100, couldperform steps 212-216. Additionally, the step 214 of generating abaseline image using the stored data sample may be accomplished by anyknown method. Those methods may include, inter alia, the Kirchhoffdiffraction stacking method (as shown in FIG. 5A and FIG. 5B), theKirchhoff wavefront smear method, wavefield synthesis, and waveequation-based methods.

Regardless of how the digital baseline image is generated, the imagingsystem 100 may receive the digital baseline image, e.g., via electronicupload or digital scan. The imaging system 100 may store the digitalbaseline image in memory for later use.

Generating Optimized Digital Images Using Holographic Imaging

Referring to steps 218-228 for generating optimized digital images, whatmay be imaged from a volume depends on the wavefield and theapplication. At least three wavefield-related items can be imaged: 1)one or more active wavefield sources, 2) the propagating wavefielditself, and 3) the propagation medium representing the wavefieldpropagation volume. The wavefield propagation volume may also bedescribed by Cartesian coordinates. Because the wavefield is recorded intime T, an image volume may also be scaled in time, then havingcoordinates X, Y, and T. Additionally, the image volume may be formed aswell using the X, Y, and Z coordinates as desired.

A wavefield originates from one or more sources, and may be detected byone or more receivers, where recordings can be made. Each data sample ofthe wavefield capture may represent a scalar or vector quantity at atime (referred to a particular time reference) and may be related tocoordinates describing location of each of the one or more sources andthe one or more receivers. Typically, this may involve at least sevencoordinates.

Although a data sampling for X, Y, Z, and/or T may be irregular or evenrandomized for one or more coordinates (whether by nature or by design),and also sparser, all concepts as explained or disclosed herein aredescribed in terms of a sampling, e.g., regular sampling, for eachcoordinate. Data treatments for imaging with other samplingdistributions, while introducing greater complexity, also may have someof the properties of methods as described herein.

For any data sample within the wavefield data capture associated withthe Cartesian propagation volume (in X, Y, and Z coordinates), the datasample may be placed within a defined “volume.” Doing so may provide forbetter organization of digital imaging computations. Because a recordinghas at least one identifiable source and receiver, that recorded signalor “trace” may be located vertically below the midpoint for thatparticular source-receiver pair as projected upward on to thepropagation volume surface. Accordingly, a recorded data volume may berepresented by coordinates X, Y, and T. Moreover, a time T at anyparticular sample from that trace may originate from responses fromdifferent voxels within the Cartesian propagation volume and also relateto different source-receiver pairings.

The views A-C of FIG. 4 illustrate the nature of a data volume from awavefield capture. FIG. 4A illustrates a voxel contribution forsource-receiver pair 1 positioned in a data capture volume below amidpoint location M1. FIG. 4B illustrates a different voxel contributionfor source-receiver pair 2 but also having midpoint location M1 andarriving also at the same time as the source-receiver pair 1. Thesource-receiver midpoints on the surface for source-receiver pairing 1and source-receiver pairing 2 may be coincident. FIG. 4C illustrates avoxel from the image volume also below position M1.

Different voxels in a propagation volume may have identical recordedtravel times for two different source-receiver pairs. Those voxels maybe positioned at the same location in the wavefield capture volume usingmidpoint positioning. Using midpoints to “locate” recordings may providean effective means for organizing and handling the data capture andcomputing image volumes therefrom.

Hence, the voxels or data samples within a data volume or wavefieldcapture may either be viewed as 1) multi-valued or 2) a “family” ofcoincident volumes related to each active separate source (orequivalently each receiver for all sources). For each family membervolume, there may in some cases, be no coincident recordings atmidpoints. A voxel in the data capture volume below a midpoint at aparticular time may represent contributions from many subsurface voxelswithin the propagation medium as shown. However, in the counterpartimage volume voxel, for X, Y, and Z or T, there may be only a singlevalue (see FIG. 4C). The imaging system 100 may use the data capture ordata volume to form image volumes representing defined characteristicsof or within the propagation volume, and more effectively according toone or more defined optimization criteria as will be discussed below.

Holography involves the encoding of some, or preferably all, wavefieldinformation within a propagation volume. If a single voxel within aCartesian propagation volume represents a point reflector or diffractor,acting according to Huygen's principle, like a new source, the voxel mayspread an illuminating wavefield out in all directions. Accordingly,recorded contributions arising from that voxel within the full wavefieldor data capture may be used to generate an image related to that voxel.That voxel may represent an elemental component of a body or some otherphysical structure.

Voxel information “encoding” within a recorded wavefield capture may betreated as holographic because of its physical nature. The voxelinformation may be used with digital methods of information recovery forimaging. FIG. 5A shows a diagram of such radiation encoding for a singlefeatured voxel or point diffractor within a uniform propagation volumeilluminated from directly above it. The geometry of the wavefieldsignature for a spike-like source has the form of an expanding sphericaldownward traveling wavefield which on encountering the featured voxel,reflects another expanding spherical wavefield (see FIG. 5B), part ofwhich travels upward to the surface of the volume where it may berecorded. This behavior is precisely as described by Huygen's Principle.

To recover an image of the voxel from the recorded wavefield, allsurface recordings for such information in the data capture or datavolume may be analyzed. FIG. 5C illustrates imaging for a subsurfacevoxel of FIGS. 5A-B using a hyperboloid of revolution imaging surface inthe data volume for a common source-receiver separation by summing therecorded wavefield values from trace intersections with this surface.The hyperboloid surfaces may be different for each constantsource-receiver separation and become “flatter” as the separationincreases.

Three recording locations represented by their midpoints (M1, M2, andM3) are shown in FIG. 5C, as vertical traces that intercept thewavefield hyperboloid of revolution imaging surface at different points.Summing the recorded wavefield values at those intersection points intothe hyperboloid apex location forms the image voxel. The moreintersections, the better the image.

Hence, following one method of wavefield data capture, recordedinformation from everywhere on this surface may be gathered from withinthe data capture to image a particular voxel, e.g., “parent” or featuredvoxel, from the propagation volume. Accordingly, recorded informationmay be gathered for all surface receiver recordings for each source,summing such information from interceptions with the appropriatehyperboloid surfaces as estimated, into the apex for each parent voxelas a simple means of forming its image.

Thus, each image voxel, in turn, would be treated as the featured voxelin the propagation volume, and for each surface midpoint location of thedata capture, different imaging surfaces must be used for the differentmembers of the data capture, as related to the source and receiverseparations. This method results in summing great numbers of samplescontaining information from a parent voxel back into that voxel as apart of developing a full image volume voxel by voxel.

Another aspect of this the methods herein is to recognize that theproperties of a real-world application for forming images from arecorded wavefield capture which has been discretely sampled,encompasses by its nature limitations and particular requirements. Thoselimitations and requirements may impact both the economics andefficiency of a data acquisition or capture, as well as the attainableresolution and quality of the final optimized image. The followingdiscussion describes the processes from data capture through to imagingof propagation volume, providing optimal results via field practices,processing, computational algorithms, improved data and interpretivedisplays.

Every data sample or voxel of a wavefield capture represents one or morevalues, and has at least one source position, a receiver position, and atime as measured from a common reference. For purposes of data handlingand analysis, voxel positions may be assigned in the data volume so arecorded data sample may be located within the data volume at a voxellocation directly below an associated source-receiver midpoint,following the midpoint convention (see FIGS. 4A-B). The data sample maybe used to 1) identify sources (as in stars for astronomy, ormicro-seismic emissions as from subsurface rock fracturing positions),2) the propagating wavefield (as in radar and sonar systems), and/or 3)the propagation medium itself (as sometimes sought in non-destructivetesting, medical imaging, and exploration seismology).

As discussed, an image space or image volume may be coincident in wholeor part with both a propagation volume and a data capture or a datavolume. Returning to steps 218-228 of FIG. 202 , image volumes may beformed within the propagation volume using the data capture volumeaccording to analytic formalisms of processing data to generateoptimized digital images. Steps 218-228 may provide input data samplingsand imaging computations. Both field data sampling and discrete imagesampling may be used in establishing comparisons between a baselineimage and an optimized image.

In step 218, the imaging system 100 may retrieve a set of rules orparameters applicable to a type of holographic computational method. Theholographic computational method may be from a group including, interalia, the Kirchhoff diffraction stacking method, the Kirchhoff wavefrontsmear method, wavefield synthesis, and wave equation-based methods. Theselected holographic computational method may be selected by a systemoperator or pre-configured in the imaging system 100.

Regarding step 218, there are many alternate methods by which the datacapture or volume may be transformed into an image volume. It is veryinstructive however, to use some of the more common imaging approaches,using simplified scenarios, as vehicles to illustrate subtle elementsarising from the discrete nature of the entire process. FIG. 4 shows howa single voxel, for the illumination by a single source, imparts itspresence into the collective wavefield. This encoding of information isholographic as discussed above. For purposes of imaging that same voxel,the system 100 may assess the voxel's character from data from therecorded wavefield using the Kirchhoff diffraction stacking method orthe Kirchhoff wavefront smear method. Both methods were developed byGustav Kirchhoff (see Seismic Data Processing, Lecture 15, 2014).

Concerning a voxel of interest in a uniform propagation volume, as shownin FIGS. 5A-B, the effect of that voxel in the data capture is describedby a surface in the form of a hyperboloid of revolution (for theconstant velocities) in FIG. 5C. Imaging may be performed by gatheringthe voxel expressions in the wavefield using the surface as shown FIG.5C, and its interceptions with the vertically plotted recorded datawithin the data capture. Similar operations may be done for every voxel,and for every source-receiver separation. For every source, the datasamples addressed in generating the image are at a greater depth (ortime) than the voxel of interest. This method of imaging may be known asthe Kirchhoff diffraction stack method in seismic exploration imaging.

Alternatively, the Kirchhoff wavefront smear method may also be used togenerate a digital image of a propagation volume. Referring to FIG. 6 ,drawing again on the data volume for a particular source, the traveltime at the receiver shown (for a constant velocity) may have derivedfrom any point on the half ellipsoid of revolution surface. Summing therecorded value for the travel time into every image voxel whichintercepts the ellipsoidal surface of revolution related to that time,may enhance and recover the individual voxel signatures, and again formimages. The wavefront smear method may distribute information only tosamples above the sample of greatest depth (or earlier in time) on anysuch imaging surface.

Similar procedures in other particular imaging applications, have othernames or descriptors like “Synthetic Aperture” exploit analogous ideasof information enhancement. There is also a prevailing perception thatall such methods, e.g., Kirchhoff diffraction stacking method andKirchhoff wavefront smear method, are essentially equivalent in theirimaging performance. However, for discrete wavefield samplings thesenotions are not correct. To attain better sampling at the greatestrecorded depths or recorded times, the Kirchhoff diffraction stackingmethod should not be used alone. Conversely, the Kirchhoff wavefrontsmear method would perform poorly for the earliest recording times orshallowest depths. These observations exemplify some of the specialrequirements stemming from the nature of discretely sampled Wavefielddata, as considered here.

In step 220, the imaging system 100 may select from the database datafor a subsequent subset of samples (“Samples_(i″)”) based on technicalparameters. The technical parameters may be categorized as fieldsampling parameters (710, FIG. 7B), image sampling parameters (712, FIG.7B), and image quality parameters (714, FIG. 7B). The imaging system 100may filter the data selection for Samples_(i) by field samplingparameters (702, FIG. 7A), image sampling parameters (704, FIG. 7A), andimage quality parameters (706, FIG. 7A).

Image quality (“IQ”) may represent one or more qualities orcharacteristics common between two images, e.g., a baseline digitalimage and a subsequent digital image, for comparison. An image qualityparameter value may represent amplitude, signal-to-noise ratio,resolution, color, frequency, or correlation. For example, IQ measuresmay be based on likeness to a baseline digital image. IQ may be ameasure in determining imaging effectiveness, and even acceptability.Thus, IQ may be a key component of optimization.

Once the imaging system 100 receives instructions to image a portion ofa propagation volume, the imaging system 100 may assess associated datasamples and image samples. It should be understood that there may bemore data samples than image samples. Thus, a ratio M of data samples toimage samples may be greater than 1—and, preferably the bigger M is thebetter.

The significance of IQ and ratio M may be illustrated in how an intendedimaging procedure will draw upon all the parameters of consequence (seeFIG. 7B and FIG. 8B), e.g., technical parameters and businessenvironment parameters. Referring to FIG. 2 , considerations for IQ andthe ratio M may be applied to steps 218-228.

Consider a data volume stored in a database (from step 210). The datavolume may represent all wavefield sources and receivers on an upperpropagation volume surface. Assuming that all wavefield sources andreceivers occupy proximate, regular positions (FIG. 3 ), the sources andreceivers may each occupy a 10 sample by 10 sample rectangular grid.Such a scenario is shown in FIG. 3 , with the two sampling grids on thepropagation volume surface shown as being nearly coincident.

Also, assuming each recorded signal may be taken to include 100 timesamples, each source, when discharged, would produce 100 recordings.There may be 100 sources, and each recording taken to have 100 samples.In all, 1 million data points would be acquired for the data capture instep 210. A standard convention (which follows principally from signalprocessing) for sampling the image volume from a wavefield capture maybe used to “mirror” the acquisition parameters for that volume.Following this convention would lead to having an image volumerepresenting a 10 sample by 10 sample surface grid, and 100 timesamples. Hence, a resulting digital image may consist of only 10,000samples, giving a value of the ratio M=100.

Because implementing sources and receivers on survey fields usuallyinvolve cost and effort, it may be obvious to ask whether a smallervalue for ratio M (and hence a cheaper and faster acquisition or survey)could still produce an acceptable image. Indeed, there is another optionunder the holographic view. Suppose the imaging system 100 samples theimage volume of the previous discussion with a 20 by 20 grid and use 400samples over the same recorded time interval. This now gives 160,000image samples, having a value of ratio M=6.25. Accordingly, the imagingsystem 100 may generate a subsequent digital image having far greaterresolution in both the spatial and time coordinates than the image asfirst described, but the signal to noise ratio may be reduced.

In step 222, the imaging system 100 may process the data for Samples_(i)using business environment parameters (see FIG. 8A). Businessenvironment parameters may include environmental parameters 812, legalparameters 814, operational parameters 816, financial parameters 818,and safety parameters 820 (see FIG. 8B). The business environmentparameters may have competing considerations. For example, there may beconsideration for lower costs and faster acquisition. Furthermore, theremay be parameters requiring better images with even higher resolution.Thus, the imaging system 100 may be configured to use IQ and assessreal-world business environment considerations for step 222, as well asfor portions of steps 218-228 in FIG. 2 .

As discussed above, alternative activities and considerations in digitalimaging may affect real-world operational speeds, efforts, and costs.Likewise, those activities and considerations also may have consequenceson generating subsequent digital images from Sample, in step 224.Accordingly, the imaging system 100 may process data with considerationfor business rules and/or parameters. For example, consideration may bemade for marine sound limits respecting certain marine life, medicalenergy radiation limits (as for X-rays and proximity to Radars),acceptable distances of energy sources from producing oil or gas wells,“no-go” areas such as missile testing zones or particular conservationhabitats. Marine seismic source volumes are restricted so as not to harmmarine life. Sound source levels for medical imaging must meet intensitystandards to avoid tissue damage. X-ray radiation must meet analogousstandards. Onshore seismic sources may not be discharged too close to aproductive well to eliminate the possibility of formation damage. Somesound sources may not be used at all in environmentally sensitive areas.Particular survey methods have only limited application in urban anddeveloped locations.

All such operational impediments and more must be honored by any lawfuloptimization procedure. Hence it is important to organize andacknowledge these business environment factors, e.g., parameters, moreexplicitly.

In step 226, the system 100 may compare a subsequent digital image withthe baseline digital image. The baseline digital image may be consideredto have acceptable IQ, because the baseline digital image is generatedby current standard procedures, as applied to the data capture. However,concerning image comparisons and treatments, the differences between abaseline image and an optimized image may be significant, for example,when considering changes in underlying image resolution.

Consider two images of the same volume, using different samplings fromthe same data capture, both images having 100,000 samples. On inspectionby the imaging system 100, one of those images may be identified as thereference standard. One reasonable likeness measure would be avolumetric normalized cross-correlation. A value less than one (1) maysuggest that one volume of lower correlation value may not be as good;however, in some cases, which volume may not be known. Accordingly, somecriterion must be used to identity the appropriate volume. Anotherfactor, which can be justified by physical arguments, may be that thebetter image volume should be more energetic or brighter. Thus, theimage having a greater sum of squares may be determined as beingimproved. Such comparative procedures may be implemented in step 226.

Suppose now one image has 100,000 samples while another starting fromthe same volume has 800,000 samples (step 216 versus 224, FIG. 2 ). Howshould they now be compared, to determine some measure of IQ? The imagevolumes are the same physical size, and the larger number of samplesperhaps indicates better resolution (smaller voxels), but for a givendata capture, sampling resolution improves only at the expense ofdecreasing signal-to-noise (SNR) content—often having lower values ofthe ratio M. The imaging system 100 may be configured to have one ormore rules to accept the image having fewer samples as more reliable,but for an application requiring better resolution, the trade-off forlower SNR may be preferable. IQ measures in this case might includefrequency and wave number criteria which recognize the bandwidth andwave number extension characteristics of greater resolution. Forinstance, imaging system 100 may be configured to have one or more rulesto identify the digital image having the broadest spatial and timefrequency bandwidths as better, according to percentage increasesrelated to bandwidth extensions. Such approach would also include somenoise threshold measure to unambiguously define bandwidth terminations.

Colors may be related to numerical values. The imaging system 100 may beconfigured to have one or more rules to compare and identify anoptimized digital image related to those color values. If the colorvalues are related to physical properties as opposed to estheticcriteria, there could be better color choices for producing particularlyeffective interpretive color displays.

In step 228, the imaging system 100 may determine whether the subsequentdigital image has one or more characteristics identifying that thesubsequent digital image is an improvement over the baseline digitalimage. If a characteristic is identified improved for the subsequentdigital image, the system 100 may identify the subsequent digital imageas optimized. If the subsequent digital image has no improvedcharacteristic over the baseline digital image, then the imaging system100 may repeat steps 218-228 until a subsequent digital image isidentified as an optimized digital image or until all holographiccomputational methods have been executed.

Data Presentations, Interpretive Concerns, and Illustrations

Application of steps 218-228 for generating optimized digital images ofwavefield data may decrease costs and effort while improving quality ofdigital images of subsequent surveys of propagation volumes. Theessential activity of presenting wavefield imaging results for theirintended use may be called “interpretation.” In step 230, interpretationof the digital imaging process resulting in the optimized digital imagemay be applied to a future propagation volume. By applying one or moredigital imaging parameters for the optimized digital image to surveyactivities for the future propagation volume, operators may realizelower costs and more efficient source-receiver array designs, amongother improvements related to business environment parameters.

While for some applications, computations using the image can provideinformation as is sought, for others, machine learning or ArtificialIntelligence (AI) may provide the desired answers. Many applications,however, will nevertheless require human insights and judgements, todetermine or estimate requisite intelligence from an image. For allcases, the measures ratio M and IQ as previously introduced playimportant roles, yet there are also other dimensions of importance. Onesuch dimension recognize may be introduction of quantitative measureswithin the image, where possible (step 228, FIG. 2 ). Suchcharacteristics can frequently offer advantages.

When quantitative measures can be related to absolute references, evenin approximation, such information can be significantly more valuablethan simple empirical comparisons of likeness or magnitudes. This axiomfollows from the fact that all applications of interest involvingwavefields are related to physical processes and real materials. A goodand quite common example for many wavefield applications would considerestimating propagation velocities within the Cartesian volume.Recognizing differences in velocity and even having some sense ofmagnitudes of differences might be beneficial, of course, but knowingestimates of actual velocities often can identify particular materialsand sometimes even provide vital interpretive insights. For example, howmuch faster or slower would sound travel in healthy tissue in contrastto different tumors?

For applications in exploration seismology for finding oil and gas ordefining hydrocarbon reservoirs, quantitative velocity estimates canprovide information concerning hydrocarbon presence (and oftendistinguish between gas and oil), porosity developments, presence ofrock fracture “swarms”, and when used in conjunction with observedgeometries, still additional information related to important physicalproperties. Similarly, we might ask, would imaging a far-off aircraft indetail using radar, be more advantageous than just detecting a “blip”?

While the concepts within this disclosure have been framed in verygeneral terms, it would be instructive to show concrete examples, wheremost of the principles discussed, and the steps of FIG. 2 are employedin varying ways, offering some variety of illustrations. Since anyspecific applications could involve much matter extraneous to theimportance of the imaging results, minimal discussion in this regard isincluded here, but references providing greater detail are identified.

The following seismic exploration examples in FIGS. 9-13 may beconsidered because their field implementations look much like thewavefield acquisition models as shown in FIG. 3 . FIG. 9 illustratesimages of a reflection wavefield from the transition zone subsurfacesurvey undertaken along the Louisiana coastal transition zone just tothe east of the Texas state line. A grid of sources and receivers weredeployed much like FIG. 3 . The sources and receivers covered a 5-mileby 25-mile strip bracketing the boundary from a portion of the Texascoast into to the waters of Gulf of Mexico. Recordings were arranged sothat one vertical image trace would be produced every 55 ft in eachdirection over the propagation volume, with 1250, four msec time samplesrecorded for each recorded trace. Projected cost was bid at $25,000,000.

The survey data represented a reflection wavefield from the transitionzone subsurface, using conventional procedures to form an image volumeusing step 216 in FIG. 2 . However, in planning for this survey, apreviously existing nearby 3D propagation volume (step 212, FIG. 2 ),but entirely onshore, was also considered because it had acquisitionparameters similar to the originally planned transition zone survey.Standard Imaging procedures had been applied to the existing surveyusing step 214. The image IQ for this volume was considered to be aquite good standard, and a representative Profile is shown in FIG. 9 .The acquisition cost of this onshore survey had been $65,000 per sq mi.

The data capture (step 210, FIG. 2 ) from this onshore survey was thendecimated to simulate removing every other Receiver line perpendicularto an equivalent “coastline”, and also removing three out of every fourSource positions. The result was equivalent to acquisition of traceshaving 220 ft×220 ft spacing—and representing ⅛ of the original data.Projected acquisition cost was estimated now to have been $45,000 per sqmi. The Imaging as formed from the decimated survey (step 222, FIG. 2 ),used a denser sampling to match that of the original undecimated imaging(see discussion of denser image sampling related to FIG. 9 ). Thecounterpart profile for such imaging is also shown in FIG. 9 . From suchtesting it was also determined that a volumetric cross-correlation withthe standard volume of 0.85 (a perfect match would be 1.00) would alsobe acceptable for the intended interpretations and other uses of theimaged data.

The transition zone survey as described was undertaken in practice butusing the analogously decimated field practice. Image formation used asampling as if from the originally planned undecimated survey. TheLouisiana transition zone survey and imaging as described yieldedexcellent results. The resulting image had an acceptable IQ. It shouldbe understood that a transition zone survey was considerably morecomplex and costly than a survey entirely on land. Proposed cost of thesurvey as originally planned was $25,000,000. The cost saving realizedwas $11,000,000 representing about 44% of the initially quoted figure.This work was reported in conjunction with Rudy Prince, at the 1998Offshore Technology Conference (Neidell & Prince, OTC Paper No. 8678,1998).

This simple test as presented clearly indicates that further examinationand better organization of the ideas and data can be very worthwhile.There are other widely used seismic data acquisition methods, wheresimilar ideas and procedures could have even greater economic impact.Marine surveys where receivers are placed on the sea bottom and usingsurface-towed Marine sources are very costly, but principally owing toreceiver deployment involving placement on the sea-bottom. A ratio Mrelating to data acquisition and imaging samples may be held constant byincreasing inexpensive source positions but using a less denselypopulated and less expensive receiver grid. Such a procedure may offersignificant cost savings, as well as faster operations. The ratio M maybe further reduced and still produce acceptable image IQ's with evenadded gains in image resolution. There are a number of other practicalissues in the acquisition and in the image processing which may yetintroduce additional constraints regarding interchange of sources andreceivers. So, how could we know what best to do? The systematic testinginherent in the procedure of FIG. 2 is clearly necessary.

Note now a single profile from a 3D onshore seismic survey as processedconventionally and displayed at the right-hand panel of FIG. 10 in theconventionally used imaging format. The display for the rightmost panelhaving ratio M as a reference value was deemed to be of acceptable IQfor interpretation. It shows the propagating Wavefield sampled spatiallyat 110 ft spatial intervals with 2 msec time samples. At the far left,the spatial resolution is doubled by spatial sampling now at 55 ftintervals. Now we have ratio M/2, and we observe that spatialcontinuities are clearly improved. Any method of evaluation includingspatial bandwidth measures would indicate improvement. Image pixelmagnitude measures however would see lower amplitudes, and increasedpopulation of smaller values, indicating lowered signal-to-noisecontributions. The center panel also shows 55 ft spatial sampling, butalso with 1 msec time samples (for ratio M/4 now). Only the centralpanel shows the two discordant “flat” reflector segments (marked) whichfrequently are a most important indicator of hydrocarbon presence. Howimportant then should resolution be as a factor for estimating IQ?Interpretation of medical images would clearly embody similarrequirements.

Early lessons as just presented demonstrate that even for conventionalseismic imaging, much of the implicit detail and information may not bereadily perceived, owing to the inadequacy of the data displays.Surprisingly, such displays presently still dominate in the oil and GasIndustry and are widely used.

FIG. 11 shows a Glenrose Reef discovery in Houston County, Texas madestarting from conventional seismic data processing in 1985. Theunderlying data capture was a 2D grid of profiles. Empirical methodswere used to scale the seismic data quantitatively as Velocities(referred to as an “Inversion”), but with an extended visual dynamicrange (EVDR) color display format applied. We shall comment further onthis procedure shortly. The contrast in information content is mostevident. The Glenrose Reef (the Eastham State Prison Farm Field inHouston County, Texas) still produces oil today, 30 years after itsdiscovery. Similarly, an analogous Austin Chalk discovery made in the1990's is shown in FIG. 12 . Here the hydrocarbon filled porosity zonesshow themselves also as contrasting velocity drops (in orange) withinthe light blue Middle Chalk member. Such imaging may be ideal today forplanning and guiding the drilling of horizontal wells.

A more contemporary formation of interest, especially for what isconsidered unconventional exploration is the Eagle Ford formation. Inthis case the hydrocarbon production is sought within the source rockitself which while filled with hydrocarbons has rock properties oftenunfavorable for to establishing commercial production. With these newertools at hand for imaging from discrete Wavefield captures, even here weachieve remarkable visibility, especially in regard to resolution withinthat formation. When we compare the holographic Image using a standarddisplay with the EVDR color display scaled in Velocity, with colorchanges representing velocity changes of 400 ft/sec, we clearly seedetail which most often can be related to production capability (FIG. 13). Most important here also is understanding that images can often betransformed to further represent estimates of physical information wedesire, and also presented with quantitative measures.

Application of Data

In the prior disclosure, two concepts were introduced where additionaldiscussion was needed, but deferred, inversion, and the Extended VisualDynamic Range (EVDR) color display. We now provide additional insightsconcerning these methods, while also emphasizing the more generalpractical needs from imaging which they directly address. Inversionprovides for one family of imaging applications a “gateway” from therather abstract appearance and significance of imaged wavefields toconcrete estimates of physical properties such as material propagationvelocities. In medical imaging a counterpart transformation might betissue identification. EVDR is concerned with rendering greaterinterpretive visibility by the displays to the human eye of underlyingimaging information content, as well as introducing some quantification.Such displays can be critical, particularly in applications whereexperience and judgement are essential to the interpretation.

Simple inversion theory by R. O. Lindseth shows that for the seismicexploration data illustrations, a procedure of mathematical integrationof the wavefield image reflection data over the time coordinate,transforms them to represent in a qualitative sense, the propagationvelocities as had been encountered (see Geophysics, Vol. 44, No. 1,1979, pp. 3-26). Such information is limited in its time frequencycontent to frequencies perhaps above 4 or 5 Hz owing to the physics ofthe data capture. Very low frequency information components are verydifficult to include. We can develop direct information for suchfrequencies (in a limited manner) from measurements in boreholes offormation velocities, or also by measuring averaged velocities from thedata capture using geometric elements of the signal propagation.

With more complete (but also limited) descriptions of the propagationvelocities we can apply empirical and statistical methods to transformthe captured wavefield data to approximations of the propagationvelocities. Such methods can often produce quite good estimates of thepropagation velocities and are generically known as Inversions. Stillother methods for obtaining such information also exist, as for examplethose known as full waveform inversions (FWI). The image of the EagleFord formation shown in FIG. 13 presents such Inversion information, butalso using an EVDR Color display which was also just explained.

Sources and receivers may be interchanged for many imaging applications.Procedures in those applications may relate to data capture decimations.Decimations can lower costs and reduce survey completion times. Theyalso decrease signal-to-noise-levels. Performing decimations involvingcost relationships of sources and receivers may affect values of IQand/or ratio M.

When we have a data capture and subsequent Imaging with a given value ofratio M, we do not know at all, the full details of the data acquisitionactivity including costs, timing, operational issues, etc. Sources maysometimes be more expensive than receivers. This can be true forexploration seismic surveys, but also for x-rays and ultrasound, whereexpense can also translate to energy limits. In marine seismic surveyssources are cheap and can be discharged rapidly. Hence, when wecontemplate or plan any Imaging project which will employ discretemethods, the requirements and limitations must be carefully thought out.The joint use and coordination of all elements will define costs,execution time, ratio M and IQ values, as well as incorporating andincluding all necessary constraints. A reasonable approach shouldaddress all the steps and seek a solution which approaches some optimumaccording to defined criteria.

Reasonable studies to better understand the processes in concert may bemade starting with existing fully executed procedures, which haveemployed methods regarded as standard, but now testing datareconfigurations, decimations and imaging variations, to suggestapproaches to optimization in terms of cost, timing, equipmentutilization, energy volume, and any other factors of relevance.Determining practical limits as described will likely not provideprecise or fully optimized results, but better results for futureapplications. We may think of such approaches as simulated “fieldtesting”.

What is claimed as the invention is:
 1. A method of oil and gasexploration, comprising: obtaining wavefield data representingrecordings of a propagating wavefield through a geophysical volume;obtaining a reference digital image of a portion or all of thegeophysical volume generated from the wavefield data; selecting aholographic computational method of imaging the wavefield data;selecting a data subset from the wavefield data; decimating the datasubset to provide a decimated subset; generating a new digital imagebased on the selected holographic computational method of imaging andthe decimated data subset, and parameters corresponding to the datasubset selected from the group consisting of environmental parameters;and determining a quantitative difference measure between the referencedigital image and the new digital image.
 2. The method of claim 1,wherein the holographic computational method of imaging the wavefielddata is from the group consisting of the Kirchhoff diffraction stackingmethod, the Kirchhoff wave front “smear” method, wavefield synthesis,and wave equation-based methods.
 3. The method of claim 1, whereinselecting a data subset from the wavefield data is based on one or moreparameters selected from the group consisting of field sampling, imagingsampling, and image quality.
 4. The method of claim 1, furthercomprising calculating a sampling ratio by dividing a number of datasamples in the data subset by a number of image samples in the datasubset.
 5. The method of claim 1, wherein the decimated data subsetrepresents a sampling ratio less than a reference sampling ratio of thereference digital image.
 6. The method of claim 1, wherein the referenceimage has a reference image quality value.
 7. The method of claim 1,wherein generating the new digital image is also based on one or moreparameters corresponding to the data subset selected from the groupconsisting of legal parameters, operational parameters, financialparameters, and safety parameters.
 8. The method of claim 1, wherein thenew digital image has a new image quality value greater than a referenceimage quality value of the reference digital image.
 9. The method ofclaim 1, wherein determining the quantitative difference measure betweenthe reference digital image and the new digital image is based on thechanging of one or more parameters selected from the group consisting offield sampling, imaging sampling, and image quality.
 10. The method ofclaim 1, further comprising implementing an imaging survey on anotherpropagation volume based on a configuration of source array and receiverarray corresponding to the data subset.
 11. A method of oil and gasexploration, comprising: obtaining wavefield data representingrecordings of a propagating wavefield through a geophysical volume;obtaining a reference digital image of a portion or all of the throughthe geophysical volume generated from the wavefield data, wherein thereference image has a reference sampling ratio; selecting a holographiccomputational method of imaging the wavefield data; selecting a datasubset from the wavefield data; decimating the data subset to provide adecimated subset, wherein the decimated data subset represents asampling ratio less than the reference sampling ratio; generating a newdigital image based on the selected holographic computational method ofimaging and the decimated data subset; and determining a quantitativedifference measure between the reference digital image and the newdigital image.
 12. The method of claim 11, further comprisingcalculating a sampling ratio by dividing a number of data samples in thedata subset by a number of image samples in the data subset.
 13. Themethod of claim 11, wherein the new digital image has a new imagequality value greater than a reference image quality value of thereference digital image.
 14. The method of claim 11, further comprisingdetermining a quantitative difference measure between the referencedigital image and the new digital image based on the changing of one ormore parameters selected from the group consisting of field sampling,imaging sampling, and image quality.
 15. A method of digital imaging ofa geophysical volume, comprising: obtaining wavefield data representingrecordings of a propagating wavefield through the geophysical volume;obtaining a reference digital image of a portion or all of thegeophysical volume generated from the wavefield data, wherein thereference image has a reference image quality value; selecting aholographic computational method of imaging the wavefield data;selecting a data subset from the wavefield data; decimating the datasubset to provide a decimated subset; generating a new digital imagebased on the selected holographic computational method of imaging andthe decimated data subset, wherein the new digital image has a new imagequality value greater than the reference image quality value; anddetermining a quantitative difference measure between the referencedigital image and the new digital image.
 16. The method of claim 15,wherein selecting a data subset from the wavefield data is based on oneor more parameters selected from the group consisting of field sampling,imaging sampling, and image quality.
 17. The method of claim 15, whereinthe decimated data subset represents a sampling ratio less than areference sampling ratio of the reference digital image.
 18. The methodof claim 15, further comprising determining a quantitative differencemeasure between the reference digital image and the new digital imagebased on the changing of one or more parameters selected from the groupconsisting of field sampling, imaging sampling, and image quality.